A hybrid LIBS-Raman system combined with chemometrics: an efficient tool for plastic identification and sorting

Electronic waste analysis using laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF): Critical evaluation of data fusion for the determination of Al, Cu and Fe

BACKGROUND

Electronic waste (e-waste) proliferation and its implications underscore the imperative for advanced analytical methods to mitigate its environmental impact. It is estimated that e-waste production stands at a staggering 20-50 million tons yearly, of which merely 20-25% undergo formal recycling. The e-waste samples evaluated contain computers, laptops, smartphones, and tablets.

RESULTS

Forty-one samples were processed, involving the disassembly and separation of components. Subsequently, two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence (ED-XRF), were applied to quantify aluminum (Al), copper (Cu), and iron (Fe) in the e-waste samples. The samples were then analyzed after acid mineralization with 50% v v-1 aqua regia in a digester block and finally by ICP OES. A solid residue composed of Si and Ti was observed after the digestion of the samples. Multivariate calibration strategies such as partial least-squares regression (PLS), principal component regression (PCR), maximum likelihood principal component regression (MLPCR), and error covariance penalized regression (ECPR) were used for calibration. Finally, the figures of merit were calculated to verify the most suitable models. The results revealed robust models with notable sensitivity, varying from 8.98 to 35.04 Signal (a.u.)(% w w-1) -1, low Limits of Detection (LoD) within the range of 0.001-0.2 % w w-1, and remarkable relative errors ranging from 2% to 33%, particularly for Cu and Fe.

SIGNIFICANCE

Notably, the models for Al faced inherent challenges, thus highlighting the complexities associated with its quantification in e-waste samples. In conclusion, this research represents an important step toward a more sustainable and efficient future for electronic waste recycling, signifying its relevance to global environmental welfare and resource conservation.

Multi-sensor characterization for an improved identification of polymers in WEEE recycling

Polymers represent around 25% of total waste from electronic and electric equipment. Any successful recycling process must ensure that polymer-specific functionalities are preserved, to avoid downcycling. This requires a precise characterization of particle compounds moving at high speeds on conveyor belts in processing plants. We present an investigation using imaging and point measurement spectral sensors on 23 polymers including ABS, PS, PC, PE-types, PP, PVC, PET-types, PMMA, and PTFE to assess their potential to perform under the operational conditions found in recycling facilities. The techniques applied include hyperspectral imaging sensors (HSI) to map reflectance in the visible to near infrared (VNIR), short-wave (SWIR) and mid-wave infrared (MWIR) as well as point Raman, FTIR and spectroradiometer instruments. We show that none of the sensors alone can identify all the compounds while meeting the industry operational requirements. HSI sensors successfully acquired simultaneous spatial and spectral information for certain polymer types. HSI, particularly the range between (1600-1900) nm, is suitable for specific identification of transparent and light-coloured (non-black) PC, PE-types, PP, PVC and PET-types plastics; HSI in the MWIR is able to resolve specific spectral features for certain PE-types, including black HDPE, and light-coloured ABS. Fast-acquisition Raman spectroscopy (down to 500 ms) enabled the identification of all polymers regardless their composition and presence of black pigments, however, it exhibited limited capacities in mapping applications. We therefore suggest a combination of both imaging and point measurements in a sequential design for enhanced robustness on industrial polymer identification.

Portable Pyrolysis-Point Discharge Optical Spectrometer for In Situ Plastic Polymer Identification by Coupling with Machine Learning

Rapid and in situ identification of specific polymers is a challenging and crucial step in plastic recycling. However, conventional techniques continue to exhibit significant limitations in the rapid and field classification of plastic products, especially with the wide range of commercially available color polymers because of their large size, high energy consumption, and slow and complicated analysis procedures. In this work, a simple analytical system integrating a miniature and low power consumption (22.3 W) pyrolyzer (Pyr) and a low temperature, atmospheric pressure point discharge optical emission spectrometer (μPD-OES) was fabricated for rapidly identifying polymer types. Plastic debris is decomposed in the portable pyrolyzer to yield volatile products, which are then swept into the μPD-OES instrument for monitoring the optical emission patterns of the thermal pyrolysis products. With machine learning, five extensively used raw polymers and their consumer plastics were classified with an accuracy of ≥97.8%. Furthermore, the proposed method was applied to the identification of the aged polymers and plastic samples collected from a garbage recycling station, indicating its great potential for identification of environmentally weathered plastics. This portable Pyr-μPD-OES system provides a cost-effective tool for rapid and field identification of polymer types of recycled plastic for proper management and resource recycling.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Towards recycling of challenging waste fractions: Identifying flame retardants in plastics with optical spectroscopic techniques

The use of plastics is rapidly rising around the world causing a major challenge for recycling. Lately, a lot of emphasis has been put on recycling of packaging plastics, but, in addition, there are high volume domains with low recycling rate such as automotive, building and construction, and electric and electronic equipment. Waste plastics from these domains often contain additives that restrict their recycling due to the hazardousness and challenges they bring to chemical and mechanical recycling. As such, the first step for enabling the reuse of these fractions is the identification of these additives in the waste plastics. This study compares the ability of different optical spectroscopy technologies to detect two different plastic additives, fire retardants ammonium polyphosphate and aluminium trihydrate, inside polypropylene plastic matrix. The detection techniques near-infrared (NIR), Fourier-transform infrared (FTIR) and Raman spectroscopy as well as hyperspectral imaging (HSI) in the short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) range were evaluated. The results indicate that Raman, NIR and SWIR HSI have the potential to detect these additives inside the plastic matrix even at relatively low concentrations. As such, utilising these methods has the possibility to facilitate sorting and recycling of as of yet unused plastic waste streams, although more research is needed in applying them in actual waste sorting facilities.

Development of an inter-confirmatory plastic characterization system using spectroscopic techniques for waste management

Ever-accumulating amounts of plastic waste raises alarming concern over environmental and public health. A practical solution for addressing this threat is recycling, and the success of an industry-oriented plastic recycling system relies greatly on the accuracy of the waste sorting technique adapted. We propose a multi-modal spectroscopic sensor which combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy in a single optical platform for characterizing plastics based on elemental and molecular information to assist the plastic identification-sorting process in recycling industries. The unique geometry of the system makes it compact and cost-effective for dual spectroscopy. The performance of the system in classifying industrially important plastic classes counting PP, PC, PLA, Nylon-1 1, and PMMA is evaluated, followed by the application of the same in real-world plastics comprising PET, HDPE, and PP in different chemical-physical conditions where the system consumes less than 30 ms for acquiring LIBS-Raman signals. The evaluation of the system in characterizing commuting samples shows promising results to be applied in industrial conditions in future. The study on effect of physical-chemical conditions of plastic wastes in characterizing them using the system shows the necessity for combining multiple techniques together. The proposal is not to determine the paramount methodology to characterize and sort plastics, but to demonstrate the advantages of dual-spectroscopy sensors in such applications. The outcomes of the study suggest that the system developed herein has the potential of emerging as an industrial-level plastic waste sorting sensor.

Evaluation of Marker Materials and Spectroscopic Methods for Tracer-Based Sorting of Plastic Wastes

Plastics are a ubiquitous material with good mechanical, chemical and thermal properties, and are used in all industrial sectors. Large quantities, widespread use, and insufficient management of plastic wastes lead to low recycling rates. The key challenge in recycling plastic waste is achieving a higher degree of homogeneity between the different polymer material streams. Modern waste sorting plants use automated sensor-based sorting systems capable to sort out commodity plastics, while many engineering plastics, such as polyoxymethylene (POM), will end up in mixed waste streams and are therefore not recycled. A novel approach to increasing recycling rates is tracer-based sorting (TBS), which uses a traceable plastic additive or marker that enables or enhances polymer type identification based on the tracer’s unique fingerprint (e.g., fluorescence). With future TBS applications in mind, we have summarized the literature and assessed TBS techniques and spectroscopic detection methods. Furthermore, a comprehensive list of potential tracer substances suitable for thermoplastics was derived from the literature. We also derived a set of criteria to select the most promising tracer candidates (3 out of 80) based on their material properties, toxicity profiles, and detectability that could be applied to enable the circularity of, for example, POM or other thermoplastics.

Review of Element Analysis of Industrial Materials by In-Line Laser—Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is a rapidly developing technique for chemical materials analysis. LIBS is applied for fundamental investigations, e.g., the laser plasma matter interaction, for element, molecule, and isotope analysis, and for various technical applications, e.g., minimal destructive materials inspection, the monitoring of production processes, and remote analysis of materials in hostile environment. In this review, we focus on the element analysis of industrial materials and the in-line chemical sensing in industrial production. After a brief introduction we discuss the optical emission of chemical elements in laser-induced plasma and the capability of LIBS for multi-element detection. An overview of the various classes of industrial materials analyzed by LIBS is given. This includes so-called Technology materials that are essential for the functionality of modern high-tech devices (smartphones, computers, cars, etc.). The LIBS technique enables unique applications for rapid element analysis under harsh conditions where other techniques are not available. We present several examples of LIBS-based sensors that are applied in-line and at-line of industrial production processes.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

A low-cost LIBS detection system combined with chemometrics for rapid identification of plastic waste

We present, rapid and efficient identification of ten different types of post-consumer plastics obtained from a local recycling unit by deploying a low cost, compact CCD spectrometer in laser-induced breakdown spectroscopy (LIBS) technique. For this investigation, spectral emissions were collected by an Echelle spectrograph equipped with an intensified charge-coupled device (ES-ICCD) as well as a non-gated Czerny Turner CCD spectrometer (NCT-CCD). The performance is evaluated by interrogating the samples in a single-shot as well as accumulation mode (ten consecutive laser shots). The results from principal component analysis (PCA) have shown excellent discrimination. Further, the artificial neural network (ANN) analysis has demonstrated that individual identification accuracies/rates up to ~99 % can be achieved. The data acquired with ES-ICCD in the accumulation of ten shots have shown average identification accuracies ~97 %. Nevertheless, similar performance is achieved with the NCT-CCD spectrometer even in a single shot acquisition which reduces the overall analysis time by a factor of ~15 times compared to the ES-ICCD. Furthermore, the detector/collection system size, weight, and cost also can be reduced by ~10 times by employing a NCT-CCD spectrometer. The results have the potential in realizing a compact and low-cost LIBS system for the rapid identification of plastics with higher accuracies for the real-time application.

Vision-Based Sorting Systems for Transparent Plastic Granulate

Granulate material sorting is a mature and well-developed topic, due to its presence in various fields, such as the recycling, mining, and food industries. However, sorting can be improved, and artificial intelligence has been used for this purpose. This paper presents the development of an efficient sorting system for transparent polycarbonate plastic granulate, based on machine vision and air separation technology. The developed belt-type system is composed of a transparent conveyor with an integrated vision camera to detect defects in passing granulates. The vision system incorporates an industrial camera and backlight illumination. Individual particle localization and classification with the k-Nearest Neighbors algorithm were performed to determine the positions and conditions of each particle. Particles with defects are further separated pneumatically as they fall from the conveyor belt. Furthermore, an experiment was conducted whereby the combined performances of our sorting machine and classification method were evaluated. The results show that the developed system exhibits promising separation capabilities, despite numerous challenges accompanying the transparent granulate material.

Quantum cascade laser-based reflectance spectroscopy: a robust approach for the classification of plastic type

The identification of plastic type is important for environmental applications ranging from recycling to understanding the fate of plastics in marine, atmospheric, and terrestrial environments. Infrared reflectance spectroscopy is a powerful approach for plastics identification, requiring only optical access to a sample. The use of visible and near-infrared wavelengths for plastics identification are limiting as dark colored plastics absorb at these wavelengths, producing no reflectance spectra. The use of mid-infrared wavelengths instead enables dark plastics to be identified. Here we demonstrate the capability to utilize a pulsed, widely-tunable (5.59 - 7.41 µm) mid-infrared quantum cascade laser, as the source for reflectance spectroscopy, for the rapid and robust identification of plastics. Through the application of linear discriminant analysis to the resulting spectral data set, we demonstrate that we can correctly classify five plastic types: polyethylene terephthalate (PET), high density polyethylene (HDPE), low density polyethylene (LDPE), polypropylene (PP), and polystyrene (PS), with a 97% accuracy rate.

Classification of Black Plastics Waste Using Fluorescence Imaging and Machine Learning

This work contributes to the recycling of technical black plastic particles, for example from the automotive or electronics industries. These plastics cannot yet be sorted with sufficient purity (up to 99.9%), which often makes economical recycling impossible. As a solution to this problem, imaging fluorescence spectroscopy with additional illumination in the near infrared spectral range in combination with classification by machine learning or deep learning classification algorithms is here investigated. The algorithms used are linear discriminant analysis (LDA), k-nearest neighbour classification (kNN), support vector machines (SVM), ensemble models with decision trees (ENSEMBLE), and convolutional neural networks (CNNs). The CNNs in particular attempt to increase overall classification accuracy by taking into account the shape of the plastic particles. In addition, the automatic optimization of the hyperparameters of the classification algorithms by the random search algorithm was investigated. The aim was to increase the accuracy of the classification models. About 400 particles each of 14 plastics from 12 plastic classes were examined. An attempt was made to train an overall model for the classification of all 12 plastics. The CNNs achieved the highest overall classification accuracy with 93.5%. Another attempt was made to classify 41 mixtures of industrially relevant plastics with a maximum of three plastic classes per mixture. The same average classification accuracy of 99.0% was achieved for the ENSEMBLE, SVM, and CNN algorithms. The target overall classification accuracy of 99.9% was achieved for 18 of the 41 compounds. The results show that the method presented is a promising approach for sorting black technical plastic waste.

A hyphenated echelle LIBS-Raman system for multi-purpose applications

We have developed and standardized a novel hybrid laser-induced breakdown spectroscopy (LIBS)-Raman system using a single pulsed laser and a high-resolution intensified charge coupled device coupled echelle spectrograph. LIBS and Raman spectroscopy are highly complementary techniques which yield elemental and molecular information. Both techniques share an apparently similar instrumental configuration but need entirely different requirements like spectral range covered, resolution, and light-gathering efficiencies. There are thus many challenges to be faced in developing a combined system. In the present work, we show that an echelle spectrograph combined with a compact Q-switched Nd:YAG laser operating at 532 nm as an excitation source in a portable configuration can be efficiently used for such multi-purpose spectroscopy. Atomic and molecular emissions from the sample surface have been recorded in a gated mode using this setup. Compared to conventional spectrographs, echelle provides simultaneous broad bandpass (250-900 nm) and better spectral resolution at an extremely small fixed slit width of 10 × 50 μm without moving the dispersive elements. The echelle-based hyphenated system provides fast and reliable analysis of materials with combined atomic and molecular spectra of the same spot with better reliability. In this paper, we discuss the optimization of various instrumental parameters and optical components of this hyphenated system using a medium Raman cross section sample, CaCO₃. The feasibility of single shot LIBS-Raman measurement capabilities of echelle has also been demonstrated using the developed system.

Combining Raman and laser induced breakdown spectroscopy by double pulse lasing

A new approach combining Raman spectrometry and laser induced breakdown spectrometry (LIBS) within a single laser event was suggested. A pulsed solid state Nd:YAG laser running in double pulse mode (two frequency-doubled sequential nanosecond laser pulses with dozens microseconds delay) was used to combine two spectrometry methods within a single instrument (Raman/LIBS spectrometer). First, a low-energy laser pulse (power density far below ablation threshold) was used for Raman measurements while a second powerful laser pulse created the plasma suitable for LIBS analysis. A short time delay between two successive pulses allows measuring LIBS and Raman spectra at different moments but within a single laser flash-lamp pumping. Principal advantages of the developed instrument include high quality Raman/LIBS spectra acquisition (due to optimal gating for Raman/LIBS independently) and absence of target thermal alteration during Raman measurements. A series of high quality Raman and LIBS spectra were acquired for inorganic salts (gypsum, anhydrite) as well as for pharmaceutical samples (acetylsalicylic acid). To the best of our knowledge, the quantitative analysis feasibility by combined Raman/LIBS instrument was demonstrated for the first time by calibration curves construction for acetylsalicylic acid (Raman) and copper (LIBS) in gypsum matrix. Combining ablation pulses and Raman measurements (LIBS/Raman measurements) within a single instrument makes it an efficient tool for identification of samples hidden by non-transparent covering or performing depth profiling analysis including remote sensing. Graphical abstract Combining Raman and laser induced breakdown spectroscopy by double pulse lasing.